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chatcut-inc/agent-plugin108 installs

image-gen

AI image generation and reference-image editing via GPT Image 2.5 Flare/Sunburst, GPT Image 2, and Nano Banana. Use when the user wants to generate or create an image / picture / still through the backend image-generation jobs.

How do I install this agent skill?

npx skills add https://github.com/chatcut-inc/agent-plugin --skill image-gen
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The image-gen skill facilitates AI image generation and editing using backend models from established providers like OpenAI and Google. It includes appropriate model selection logic, parameter mapping, and billing transparency. The skill employs security best practices by delegating the resolution of sensitive asset data to the backend system rather than processing raw bytes locally. No malicious code, obfuscation, or persistence mechanisms were detected.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

Image Gen

If submit_image is not loaded yet, load it with ToolSearch before calling it, so you use its real parameters instead of guessing.

ChatCut image generation and editing require an active paid Pro subscription and use credits for every model. If submit_image returns FEATURE_NOT_INCLUDED, surface the upgrade requirement; do not retry another ChatCut model to bypass it. Codex native image generation keeps its own host entitlement and is not governed by this ChatCut requirement.

Generate AI images through the backend generation API. Submit-only: creates a generation job and returns a jobId.

After submission, use track_progress tool to check status or wait for completion.

Model Selection

ModelStrengthsMax refs
gpt-image-2.5-flareDefault: fast everyday generation and editing10
gpt-image-2.5-sunburstPrecision edits and detailed creative work10
gpt-image-2Previous GPT image model10
nano-bananaStrongest reference-image fidelity14
  • gpt-image-2.5-flare is the default. Honor the image model attached to the user prompt or explicitly requested.
  • Use gpt-image-2.5-sunburst when the user selects it for precise editing.
  • nano-banana is the reference-heavy choice. Use it when reference-image fidelity matters more than text rendering, or when the user needs more than 10 reference images.

IMPORTANT: Before generating, READ the model's reference document for params, limits, and prompt tips:

Tool Params

ParamValuesDefault
aspectRatio1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 4:5, 5:4, 21:916:9
imageSize1K, 2K, 4K1K
qualitylow, medium, high, auto; Image 2.5 also xhigh, maxhigh
backgroundauto, opaque, transparent (Image 2.5 only; PNG output)auto
referenceAssetIdsArray of project asset ids — backend resolves bytes server-side—
nameShort descriptive asset name shown in the library—
countNumber of images to generate (1–10, each becomes a separate job)1

Defaults

  • Aspect ratio: 16:9. If the project composition is not 16:9, ASK the user which aspect ratio they want before generating.
  • Size: 1K.

Ask Before Submit

  • Never auto-upgrade size.
  • Only pass imageSize: "2K" or "4K" when the user explicitly asks. Warn that 2K/4K are EXPERIMENTAL and may be slower.

Reference Images

Use when the user provides source material to edit, blend, or use as visual guidance (e.g. "change the background", "combine these into a poster").

  • Pass project asset ids via referenceAssetIds. The backend fetches and encodes them server-side — never pull the asset bytes yourself.
  • When the user @-references an image asset, pass its id directly in referenceAssetIds.
  • Formats accepted by backend: png, jpeg, webp, svg (auto-rasterized to png), heic, heif. Each ≤ 50MB.

Run

// Basic generation
submit_image({
  model: "gpt-image-2.5-flare",
  prompt: "a cute orange cat",
  name: "Cat",
});

// With quality (OpenAI models)
submit_image({
  model: "gpt-image-2.5-flare",
  prompt: "hero poster with bold title",
  quality: "high",
  name: "Hero Poster",
});

// With reference images — pass project asset ids; backend resolves bytes
submit_image({
  model: "gpt-image-2.5-flare",
  prompt: "change background to beach",
  referenceAssetIds: ["<assetId>"],
  name: "Beach Edit",
});

// Reference-heavy with nano-banana
submit_image({
  model: "nano-banana",
  prompt: "composite poster",
  referenceAssetIds: ["<id1>", "<id2>"],
  name: "Composite",
});

// Multiple images
submit_image({
  model: "gpt-image-2.5-flare",
  prompt: "product shots",
  count: 3,
  name: "Product",
});

After submission, call track_progress with action=status jobIds=<jobId>. If the current task depends on a non-terminal result, call action=wait and do what its result says: where automatic follow-ups are supported it registers one and you end your turn; otherwise it returns sleep-and-recheck guidance. Neither action blocks.

Rules

  • Always provide name with a short descriptive asset name.
  • Wait for completion unless the user only asked to queue. Use returned asset ids for further edits; use edit_item to place an image on the timeline when requested.
  • Generation costs credits. Before submitting, briefly tell the user what you're about to generate — especially when generating multiple images.
  • Do not use this skill for job management. Use track_progress tool for that.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/chatcut-inc/agent-plugin/image-gen">View image-gen on skillZs</a>